# Self-Annotated Training for Controllable Image Captioning

arXiv: https://arxiv.org/abs/2110.08446 · HTML: https://mlsanity.com/paper/2110.08446/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 39 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [Sequence Level Training with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06732/) (2015)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [Unified Vision-Language Pre-Training for Image Captioning and VQA](https://mlsanity.com/paper/1909.11059/) (2019)
- [Meshed-Memory Transformer for Image Captioning](https://mlsanity.com/paper/1912.08226/) (2019)
- [Attention on Attention for Image Captioning](https://mlsanity.com/paper/1908.06954/) (2019)
- [Auto-Encoding Scene Graphs for Image Captioning](https://mlsanity.com/paper/1812.02378/) (2018)
- [X-Linear Attention Networks for Image Captioning](https://mlsanity.com/paper/2003.14080/) (2020)
- [Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs](https://mlsanity.com/paper/2003.00387/) (2020)
- [Improving Image Captioning with Better Use of Captions](https://mlsanity.com/paper/2006.11807/) (2020)
- [Dual-Level Collaborative Transformer for Image Captioning](https://mlsanity.com/paper/2101.06462/) (2021)
- [Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions](https://mlsanity.com/paper/1811.10652/) (2018)
- [Normalized and Geometry-Aware Self-Attention Network for Image Captioning](https://mlsanity.com/paper/2003.08897/) (2020)
- [Towards Diverse and Natural Image Descriptions via a Conditional GAN](https://mlsanity.com/paper/1703.06029/) (2017)
- [Fast, Diverse and Accurate Image Captioning Guided By Part-of-Speech](https://mlsanity.com/paper/1805.12589/) (2018)
- [Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network](https://mlsanity.com/paper/2012.07061/) (2020)
- [Human-like Controllable Image Captioning with Verb-specific Semantic Roles](https://mlsanity.com/paper/2103.12204/) (2021)
- [Length-Controllable Image Captioning](https://mlsanity.com/paper/2007.09580/) (2020)
- [Intention Oriented Image Captions with Guiding Objects](https://mlsanity.com/paper/1811.07662/) (2018)
- [Improving Image Captioning with Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1805.07112/) (2018)
- [Self-critical n-step Training for Image Captioning](https://mlsanity.com/paper/1904.06861/) (2019)

## Built on by (most cited first, 2 of 2)

- [Learning Combinatorial Prompts for Universal Controllable Image Captioning](https://mlsanity.com/paper/2303.06338/) (2023)
- [Improving Image Captioning with Control Signal of Sentence Quality](https://mlsanity.com/paper/2206.03196/) (2022)

## Abstract (arXiv)

The Controllable Image Captioning (CIC) task aims to generate captions conditioned on designated control signals. Several structure-related control signals are proposed to control the semantic structure of sentences, such as sentence length and Part-of-Speech tag sequences. However, due to the fact that the accuracy-based reward focuses mainly on contents rather than semantic structures, existing reinforcement training methods are not applicable to structure-related CIC models. The lack of reinforcement training leads to exposure bias and the inconsistency between the optimizing function and evaluation metrics. In this paper, we propose a novel reinforcement training method for structure-related control signals: Self-Annotated Training (SAT), to improve both the accuracy and controllability of CIC models. In SAT, a recursive annotation mechanism (RAM) is designed to force the input control signal to match the actual output sentence. Moreover, we propose an extra alignment reward to finetune the CIC model trained after SAT method, which further enhances the controllability of models. On the MSCOCO benchmark, we conduct extensive experiments on different structure-related control signals and on different baseline models, the results of which demonstrate the effectiveness and generalizability of our methods.